Research within the agricultural community has shown that management of crop production may be optimized by taking into account spatial variations that often exist within a given farming field. For example, by varying the farming inputs applied to a field according to local conditions within the field, a farmer can optimize crop yield as a function of the inputs being applied while preventing or minimizing environmental damage. This management technique has become known as precision, site-specific, prescription or spatially-variable farming.
The management of a field using precision farming techniques requires the gathering and processing of data relating to site-specific characteristics of the field. Generally, site-specific input data is analyzed in real-time or off-line to generate a prescription map including desired application or control rates of a farming input. A control system reads data from the prescription map and generates a control signal which is applied to a variable-rate controller for applying a farming input to the field at a rate that varies as a function of the location. Variable-rate controllers may be mounted on agricultural vehicles with attached variable-rate applicators, and may be used to control application rates for applying seed, fertilizer, insecticide, herbicide or other inputs. The effect of the inputs may be analyzed by gathering site-specific yield and moisture content data and correlating this data with the farming inputs, thereby allowing a user to optimize the amounts and combinations of farming inputs applied to the field.
The spatially-variable characteristic data may be obtained by manual measuring, remote sensing, or sensing during field operations. Manual measurements typically involve taking a soil probe and analyzing the soil in a laboratory to determine nutrient data or soil condition data such as soil type or soil classification. Taking manual measurements, however, is labor intensive and, due to high sampling costs, provides only a limited number of data samples. Remote sensing may include taking aerial photographs or generating spectral images or maps from airborne or spaceborne multispectral sensors. Spectral data from remote sensing, however, is often difficult to correlate with a precise position in a field or with a specific quantifiable characteristic of the field. Both manual measurements and remote sensing require a user to conduct an airborne or ground-based survey of the field apart from normal field operations.
Spatially-variable characteristic data may also be acquired during normal field operations using appropriate sensors supported by a combine, tractor or other vehicle. A variety of characteristics may be sensed including soil properties (e.g., organic matter, fertility, nutrients, moisture content, compaction, topography or altitude), crop properties (e.g., height, moisture content or yield), and farming inputs applied to the field (e.g., fertilizers, herbicides, insecticides, seeds, cultural practices or tillage parameters and techniques used). Other spatially-variable characteristics may be manually sensed as a field is traversed (e.g., insect or weed infestation or landmarks). As these examples show, characteristics which correlate to a specific location include data related to local conditions of the field, farming inputs applied to the field, and crops harvested from the field.
Site-specific farming systems are typically used to perform functions such as those described above (e.g., yield mapping or variable-rate application). It would be desirable, however, to use components of a site-specific farming system to automate control functions of agricultural vehicles that are normally controlled manually. For example, the position of a hitch assembly coupled to a tractor is usually set by an operator with an input device such as a draft force or position command device, with a discrete input switch provided to allow the operator to manually raise and lower the hitch under certain conditions, such as when the hitch is raised when turning at a headland or driving between fields. The operator may also use the discrete input switch to raise the hitch to protect the attached implement from being damaged by obstructions (e.g. rocks or irrigation pipes) at or near the surface of a field. For another example, the position of a header supported by a combine is manually raised and lowered under certain conditions.
Accordingly, it would be desirable to automate these manually-controlled functions to decrease the workload of the operator and to increase system accuracy. For example, an operator may become fatigued or overloaded by the need to manually actuate a switch to raise and lower a hitch as a tractor turns repetitively at the headlands of a field. A portion of the field may not be worked when a hitch is not manually lowered in time to engage the ground at the start of the field, or a portion of the headland may be inadvertently worked if the hitch is not raised in time. A plow may be damaged if not raised above an obstruction (e.g., a rock, pipe, etc.) in the field because the operator was unaware of the location of the obstruction or because the operator forgot to raise the hitch.